The doughnut-shaped radiation energy pattern of orbital angular momentum (OAM) beams has been a longstanding challenge in electromagnetic (EM) vortex imaging. In this paper, an effective EM vortex imaging scheme is developed based on the orthogonal polyphase-coded OAM multiplexing beam with mitigated beam energy divergence. Firstly, vortex EM waves are modulated with different orthogonal polyphase codes and transmitted simultaneously to illuminate the imaging scenario. The codes change the phase relationship between different transmitted signals, leading to a convergent energy distribution around the beam axis. Then, an OAM demultiplexing method is proposed to separate the radar echo of each OAM mode, which maintains the individual spiral phase fronts at the same time. Finally, the two-dimensional fast Fourier transform (FFT) is applied to the demodulated signals to reconstruct the target image. Simulation results show that the proposed imaging method has a higher azimuth resolution than the traditional method with small elevation angles and performs much better in low SNR environments. Furthermore, an experiment in an anechoic chamber validates the potential of the proposed method. This work can benefit the practical applications of OAM imaging technology.

Detecting targets in dynamically changing clutter environments has always been challenging for radar techniques. The innovative matrix information geometry-based (MIG) detector, which equips with different geometric distances to discriminate the target and clutter on Hermitian positive definite (HPD) manifold, treats the detection issue from a geometric point of view, however, the current geometric distances do not always perform satisfactory under the changing environment. To address this problem, this paper proposes an adaptive Log-Euclidean (LE) metric on the manifold to properly match the actual clutter environment and achieve satisfactory performance. Specifically, the adaptive LE metric is developed on a dimension-reduced manifold by deriving a discriminative manifold transformation, and the determination of the transformation is formulated as a joint optimization problem between the cell under test (CUT) and the clutter centroid. In this study, manifold-based methods and eigen-decomposition-based method are proposed to solve the optimization problem. Moreover, an adaptive LE metric-based MIG detector is designed, and its bounded constant false alarm rate (CFAR) property is analyzed. At last, both simulated data and real radar data are utilized to validate the performance of the proposed method. Particularly, in unmanned aerial vehicle (UAV) target detection experiment, the results show that the strong clutter is effectively suppressed and the target is simultaneously highlighted without any prior knowledge

Accurate time–frequency (TF) feature extraction of moving target is a challenging task due to the poor resolution and serious cross-terms of the conventional TF analysis (TFA) methods. In this letter, an effective TFA algorithm based on the adaptive short-time sparse representation (ASTSR) is proposed to enhance the TF feature of moving target. First, the limitation of the Fourier-transform-based short-time TFA is revealed from the motion approximation perspective. Then, to achieve accurate motion approximation, the width of the analysis window is determined adaptively by minimizing the bandwidth of each short-time signal individually. Finally, TF representation (TFR) with high energy concentration is obtained using the sparsity of these signal segments in the chirp dictionary. Comparisons indicate that the ASTSR provides high-resolution TFRs without producing interference terms at an acceptable computational cost while performing well in weak component expressing and signal denoising. Furthermore, an ISAR imaging example confirms the potential of the proposed method.

In this letter, an effective parametric IF estimation method based on piece-wise sparse representation (PWSR) is proposed to enhance the precision of conventional time-frequency distribution (TFD)-based estimators. Firstly, the signal is divided into a series of short-time segments according to the piece-wise quadratic frequency modulation (QFM) model. Then, the corresponding QFM parameters of each segment are accurately estimated by solving a sparse representation (SR) problem. Moreover, a construction scheme enabling the QFM dictionary to vary adaptively with the analyzed short-time signal is also provided. Finally, the IF of each segment is reconstructed according to the SR solution individually and combined together to generate the complete IF estimates. A radar imaging example verifies that the proposed method achieves a notable improvement in estimation accuracy as compared to existing TFD-based approaches.

In recent years, electromagnetic (EM) vortex imaging has emerged as a novel technique in the field of radar super-resolution imaging. This approach fundamentally differs from conventional methods by the relative lateral motion between the target and the platform. Although the super-resolution azimuthal imaging can be achieved, different orbital angular momentum (OAM) modes should be modulated in different signal pulses. In contrast, a new OAM modulation and monopulse imaging method is proposed in this paper, which involves multiplexing OAM modes based on frequency diversity in one pulse and can considerably improve the imaging efficiency. By introducing tiny frequency offsets among the elements of a uniform circular frequency diverse array (UC-FDA), the OAM multiplexing beams are generated. Subsequently, the EM vortex imaging model for coherent UC-FDA is established, and the imaging method is proposed. The theoretical performance analyses of spatial azimuthal resolution and efficiency are derived. Finally, the results demonstrate that the proposed method exhibits comparable imaging performance to the conventional EM vortex imaging method, even when only a single pulse is transmitted.

The wavefront modulated electromagnetic (EM) wave has important applications in radar imaging realms, especially for the forward-looking sight. To achieve good azimuth resolution and high signal-to-noise ratio (SNR), the modulated field is expected to have space-dependent phase front and converged intensity distributions simultaneously. In this paper, a novel wavefront modulation method and its application for object imaging are developed. Firstly, the generation method of wavefront modulated field based on Fourier synthesis is proposed to design the radiation field, where the wavefront phase changes linearly as a function of the cross-range angle and the energy is converged. Subsequently, the imaging method based on this wavefront modulated field is proposed. Results indicate the effectiveness of the imaging scheme, which is robust against the noise influence.